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Jurnal Algoritma
Published by Institut Teknologi Garut
ISSN : 14123622     EISSN : 23027339     DOI : https://doi.org/10.33364/algoritma
Core Subject : Science,
Computer Science & IT
Jurnal Algoritma merupakan jurnal yang digunakan untuk mempublikasikan hasil penelitian dalam bidang Teknologi Informasi (TI), Sistem Informasi (SI), dan Rekayasa Perangkat Lunak (RPL), Multimedia (MM), dan Ilmu Komputer (Computer Science).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,150 Documents
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Pengukuran Kualitas Pelayanan Polsek Simpang Empat Terhadap Kepuasan Masyarakat Dengan Metode SAW Sri Helena Utami Saragih; William Ramdhan; Akmal
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3329

Abstract

Public service is a key indicator in assessing the performance of government agencies, including the police. The Simpang Empat Police Station, as a technical police unit at the subdistrict level, plays a strategic role in providing services to the community. However, there are still complaints regarding long wait times, unfriendly officers, and suboptimal facilities. This study aims to evaluate the quality of public service at the Simpang Empat Police Station using the Simple Additive Weighting (SAW) method. The assessment was based on six criteria: service accuracy, officer attitude and behavior, service speed, clarity of information, transparency of the service process, and communication with the public. Data was collected through a questionnaire administered to service users. The results indicate that Mediation Services (A07) received the highest preference score of 0.9865, followed by Security and Order Services (A02) at 0.9792 and Guidance and Counseling Services (A05) at 0.9752. The average SAW score was 0.9706 with a satisfaction level of 97.06%, which falls into the “very satisfactory” category, indicating that the SAW method is effective for evaluating the quality of public services.
Tinjauan Sistematis Klasifikasi Motif Batik: Reduksi Noise Gaussian, Kernel Similarity, dan Ensemble Learning (Voting Classifier) Aji Priyambodo; R. Rizal Isnanto; Ridwan Sanjaya
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3332

Abstract

Klasifikasi motif batik merupakan persoalan pengenalan pola tekstur halus yang dipengaruhi kualitas citra, representasi kemiripan, dan kemampuan generalisasi model. Artikel ini menyajikan tinjauan sistematis berbasis PRISMA 2020 untuk memetakan perkembangan riset klasifikasi motif batik dengan fokus pada reduksi noise Gaussian, kernel similarity, dan ensemble learning berbasis voting classifier. Korpus disusun dari tiga berkas bibliografi jejaring sitasi Connected Papers dan penelusuran manual, menghasilkan 126 rekaman; setelah duplikasi dihapus (n=4) dan penyaringan judul-abstrak, 52 studi dimasukkan dalam sintesis. Hasil pemetaan menunjukkan dominasi CNN/deep learning dan fitur tekstur klasik seperti GLCM/LBP dengan pengklasifikasian KNN/SVM. Namun, sintesis lintas studi menunjukkan bahwa literatur masih lebih berorientasi pada optimasi akurasi in-domain pada dataset yang relatif ter kurasi dibandingkan pengujian kesiapan implementasi nyata. Kernel similarity hanya muncul pada 7 studi, ensemble/voting pada 3 studi, sedangkan reduksi noise Gaussian tidak muncul sebagai fokus eksplisit pada metadata (n=0). Temuan utama tinjauan ini ialah adanya gap paling kritis pada evaluasi lintas-domain, robustness terhadap variasi pencahayaan/noise/lipatan, serta pelaporan penanganan imbalance kelas dan interpretabilitas model. Dengan demikian, kontribusi tinjauan ini bukan hanya memetakan tren metode, tetapi juga menegaskan pergeseran agenda riset dari sekadar akurasi menuju generalisasi, robustness, dan keterjelasan model.
Model Rekomendasi Mata Pelajaran Pemintaan Siswa dengan Multi-Label Classification Hibatul Azizi; Wahyu Widadi; Mardi Hardjianto
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3337

Abstract

Pemilihan mata pelajaran pemintaan di Sekolah Menengah Atas merupakan proses penting yang mempengaruhi perkembangan akademik dan karier siswa, namun masih sering dilakukan secara subjektif. Penelitian ini mengusulkan sistem prediksi pemintaan berbasis Data Mining menggunakan pendekatan multi-label classification, sehingga siswa dapat direkomendasikan lebih dari satu pemintaan yang sesuai. Dua algoritma yang dibandingkan adalah C4.5 dan K-Nearest Neighbor (K-NN) dengan memanfaatkan data nilai rapor, hasil tes IQ, gaya belajar, dan tipe kepribadian siswa kelas X SMAN 95 Jakarta. Hasil pengujian menunjukkan bahwa K-Nearest Neighbor (K-NN)  menghasilkan akurasi lebih tinggi sebesar 95%, sedangkan C4.5 memiliki performa yang kompetitif serta keunggulan dalam interpretabilitas melalui model pohon keputusan. Secara komparatif, K-NN lebih unggul dalam aspek prediksi, sementara C4.5 lebih mendukung pengambilan keputusan yang mudah dipahami. Sistem kemudian diimplementasikan dalam bentuk aplikasi web untuk membantu guru Bimbingan Konseling dalam memberikan rekomendasi pemintaan yang lebih objektif dan fleksibel. Temuan ini menunjukkan bahwa pendekatan multi-label classification efektif dalam merepresentasikan kompleksitas preferensi siswa
Analisis Performa, Explainability, dan Fairness pada Model Klasifikasi Multi-Dataset Medis Menggunakan SVM dan Random Forest Luh Ayu Martini; Indrianto; I Kadek Seneng
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3339

Abstract

Machine learning models in the healthcare domain are capable of achieving high accuracy; however, they often function as black-box systems, making them less transparent and potentially introducing bias toward sensitive groups. This study aims to analyze the performance, interpretability, and fairness of disease classification models using five tabular medical datasets, namely Alzheimer, Obesity, Hypertension, Stroke, and Asthma datasets obtained from Kaggle. The research stages include data cleaning, feature transformation, normalization, and handling class imbalance using SMOTE. The models were developed using Support Vector Machine (SVM) and Random Forest algorithms with hyperparameter optimization through GridSearchCV and validation using 5-fold cross-validation. The results indicate that Random Forest provided the most consistent performance, achieving the highest accuracy of 96.92% on the Obesity dataset. In imbalanced datasets such as Stroke and Asthma, model performance declined, particularly in terms of precision and F1-score, due to uneven class distribution and data complexity. Interpretability analysis using SHAP and LIME demonstrated that the models utilized clinically relevant features, such as age, blood pressure, body mass index, and cognitive function indicators. Fairness evaluation using Demographic Parity Difference (DPD) and Equal Opportunity Difference (EOD) produced relatively small values, indicating that the distribution of predictions across sensitive groups, particularly gender, was fairly balanced, although still influenced by data characteristics. This study confirms that integrating performance, interpretability, and fairness in multi-dataset evaluation provides a more comprehensive approach compared to conventional evaluations that focus solely on accuracy.
Adopsi Teknologi Kecerdasan Buatan (AI) dalam Pelaporan Pajak: Studi Persepsi Kemudahan dan Keamanan Data Wajib Pajak Milenial Lina Nurlaela; Marti Dewi Ungkari
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3341

Abstract

Digital transformation in the public service sector has now expanded into tax administration through the integration of Artificial Intelligence (AI) systems. This study aims to analyze how millennial taxpayers perceive the use of AI technology in the tax reporting process, focusing on two main dimensions: system accessibility, or perceived ease of use, and personal data protection, or data security. Using a descriptive quantitative approach and SEM-PLS analysis involving 100 millennial taxpayer respondents, the findings reveal that AI technology is perceived as capable of simplifying the process of identifying and classifying tax objects. However, concerns regarding the confidentiality of personal information and the risk of data leakage remain real obstacles that limit the full adoption of the system.
Perbandingan K-Means, DBSCAN, dan Louvain pada Peserta Pendidikan Kesetaraan di Kabupaten Balangan Ida Ariyani Hasanah; Riama Simanjuntak; Arief Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3350

Abstract

This study aims to analyze the clustering patterns of equivalency education participants through a comparative clustering approach using three algorithms: K-Means (centroid-based), DBSCAN (density-based), and Louvain (graph-based). The dataset consists of 1,057 participants with numerical and categorical attributes representing heterogeneous characteristics. The research stages include data preprocessing and the implementation of clustering algorithms on the same dataset to maintain comparison consistency. Evaluation was conducted using the Silhouette Score and Davies-Bouldin Index (DBI) as internal validation metrics, as well as external validation through expert confirmation to ensure the contextual relevance of the results. The findings indicate that K-Means and DBSCAN produced a Silhouette Score of 0.040, reflecting poor cluster separation quality and the dominance of one large cluster. DBSCAN demonstrated an advantage in detecting noise; however, it was unable to significantly improve cluster separation quality in data with a high level of homogeneity. In contrast, the Louvain algorithm generated a more balanced community structure with a low imbalance ratio, making it more capable of representing relational connections among data points that are not fully captured by distance-based approaches. This study contributes through a comparative analysis across clustering approaches in the context of equivalency education, as well as through the integration of quantitative and contextual validation. The findings confirm that graph-based approaches are more adaptive for data with high homogeneity and have the potential to serve as a basis for participant segmentation to support more effective data-driven decision-making in the education sector.
Evaluasi Komparatif Neural Network dan Random Forest untuk Prediksi Produktivitas Tandan Buah Segar Kelapa Sawit Berbasis Fitur Musiman Gellysa Urva; Welly Desriyati
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3352

Abstract

Fresh Fruit Bunch (FFB) productivity in oil palm exhibits seasonal patterns that pose challenges for predictive modeling, particularly given the limited amount of data. This study aims to compare the performance of Neural Networks and Random Forests in predicting FFB productivity based on temporal features, including lag, rolling mean, and cyclical encoding. Evaluation was conducted using time-series validation with MAE, RMSE, and R² metrics. The results indicate that Neural Networks face generalization limitations with limited data, reflected in poor performance on the test data. Conversely, Random Forest delivers more stable and accurate performance with an MAE of 0.2581, an RMSE of 0.3325, and an R² of 0.9675. These findings confirm the superiority of tree-based ensemble approaches in handling seasonal data with small sample sizes. The contribution of this research is to provide empirical evidence and recommendations for more reliable models for TBS productivity prediction as a basis for developing decision support systems in the plantation sector.
Sistem E-Customer Relationship Management Berbasis Web untuk Pengelolaan Pelanggan Nur Sabrina; Dewi Anggraeni; Akmal Nasution
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3353

Abstract

Advances in information technology are driving businesses to utilize digital systems to improve the quality of customer service. Cafe Kekynian, as a culinary business, still faces challenges in managing customer data and transactions, which were previously handled manually, making it difficult to record transaction histories and monitor customer activity. This study aims to design and implement a web-based Customer Relationship Management (CRM) system to support more effective customer relationship management. The research method employed is software engineering using the Software Development Life Cycle (SDLC) approach, which includes the stages of requirements analysis, system design, implementation, and system testing. Research data was obtained through observation, interviews, and documentation conducted at Cafe Kekynian. System testing was performed using the Black Box Testing method to ensure that every system function operates in accordance with user needs. The results of the study indicate that the developed CRM system is capable of integrating customer data management, transaction recording, and purchase history storage into a single centralized database. The implementation of this system facilitates managers in monitoring customer activities, improves the efficiency of transaction data management, and supports the formulation of more effective customer service strategies.
Pengelompokan Permintaan Produk Alat Kesehatan Menggunakan K-Means untuk Jadwal Pembelian Vika Aulia Munawaroh; R Rhoedy Setiawan; Yudie Irawan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3360

Abstract

Fluctuations in the demand for medical devices can trigger the risk of stock shortages (stockouts) and overstock conditions, which may affect operational costs and the quality of distribution services. This study aims to classify medical device products at CV Patriot Kencana Medika Kudus based on demand patterns and purchasing characteristics, and to map the clustering results as an initial basis for developing purchasing schedules. The data used consist of internal purchasing transaction histories from the 2023–2025 period with four main features: Quantity, Price_Per_Unit, Lead_Time_Days, and Total_Purchase_Value. The methods applied include exploratory data analysis, feature construction and normalization, determination of the optimal number of clusters using the Elbow Method and Silhouette Score, K-Means modeling, and evaluation using the Silhouette Score and Davies–Bouldin Index (DBI). The results indicate that the use of three clusters provides the most reasonable compromise between the inertia reduction pattern, Silhouette value, and managerial interpretability. A Silhouette Score of 0.2563 and a DBI value of 1.349 suggest that the quality of cluster separation remains at a low to moderate level, meaning that the resulting clusters are more appropriately interpreted as an initial segmentation rather than a fully distinct classification. The three clusters formed were interpreted as general products, premium products, and strategic products. The numerical characteristics of each cluster were then used to calculate simple indicators, namely the reorder point (ROP) and economic order quantity (EOQ), as baseline purchasing recommendations. The main contribution of this study lies in integrating clustering results with operational inventory policy parameters, although the findings still need to be interpreted cautiously because they have not yet been compared with other algorithms, their stability has not been tested, and the EOQ model applied remains simplified.
Klasterisasi Kebutuhan Pupuk Bersubsidi Menggunakan Algoritma K-Means dan Elbow Method Nurya Herlina Sari; R.Rhoedy Setiawan; Yudie Irawan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3361

Abstract

The distribution of subsidized fertilizer at the UD Barokah Tani Kiosk in Pati Regency does not yet meet farmers’ needs due to the manual management of RDKK data. This study aims to cluster subsidized fertilizer needs using the K-Means algorithm, validated by the Elbow Method and Silhouette Score. The data used consists of 1,420 RDKK records for the 2025–2026 period, with variables including land area, UREA_TOTAL, NPK_TOTAL, and the number of commodities. The results indicate that the optimal number of clusters is k = 3, with a Silhouette Score of 0.9192, indicating very high cluster quality. The data is divided into three categories: low, medium, and high, with a dominance in the low to medium categories. This study contributes by comprehensively integrating fertilizer requirement variables and using a combination of the Elbow Method and Silhouette Score to enhance the validity of the clustering results. The clustering results are implemented in a web-based system to support rapid, data-driven analysis and visualization.

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